
from __future__ import annotations

def a__       (			A_,	A_   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__								=			set(A_   ), [start]

  while stack:
    __magic_name__								=			stack.pop()
    explored.add(A_   )
    # Differences from BFS:
    # 1) pop last element instead of first one
    # 2) add adjacent elements to stack without exploring them
    for adj in reversed(graph[v]   ):
      if adj not in explored:
        stack.append(A_   )
  return explored


__lowerCAmelCase     :	Optional[int]       			=  {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, 'A'))



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

